C2SMART researchers developed a more efficient, secure, blockchain-based system to story mobility data on a distribute ledger. To store this data at scale, researchers leverage InterPlanetary File System (OPFS), a scalable distributed peer-to-peer data storage system, and develop efficient consensus algorithms to prevent users from injecting malicious or fake trajectories into the ledger.
This project is focused on developing a deep learning based data acquisition and analytics tool using vision-based sensors (i.e., cameras) to understand cities with machine eyes.
Overview Transportation is a major source of greenhouse gas emissions and air pollution, with emissions from light-duty vehicles constituting its major share. For example, the light-duty vehicles in New York
Traffic signs are critical assets for roadway and infrastructure management. They are also in a great variety and different conditions. According to the asset management plan proposed by US DOT, the research team proposes a cost-effective approach to build a traffic sign data inventory using open street images.
A broad API will be developed to handle interfacing any simulation with a multi-agent demand simulator. This will be tested on the existing MATSim-NYC (which will be enhanced to include freight and parcel delivery activities) and aBEAM implementation, BEAM-NYC, for three use cases in electric transit, freight, and traffic.
The project will build a framework to optimize and prioritize locations for FloodNet sensor deployment, for measurement of hyper – local flooding in New York City (NYC).
This project is a continuation of a C2SMART funded project from 2021 titled “Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems.” In the phase 1 project, the team built a digital shadow of campus civil infrastructure and visualized impacts of construction project schedule on the surrounding transportation infrastructure. Phase 2 is focused on expanding the work accomplished in phase 1, to extend the digital twin and enable live data feeds.
The COVID-19 outbreak has dramatically changed travel behavior in cities across the world. With changed travel demand, economic activity, and social-distancing/stay-at-home policies, transportation systems have experienced an unprecedented shift in demand and usage. Since the start of the pandemic, the C2SMART research team has been collecting data and investigating the impact of COVID-19 on mobility and sociability.
The main objective of this study is the assessment of the Construction Impact Analysis (CIA) and Work Zone Impact and Strategy Estimator (WISE) tools, and determination of the feasibility of their customization with respect to New York City Department of Transportation (NYCDOT) and New York State Department of Transportation (NYSDOT)’s needs and requirements, cost of adoption and modification, and related issues.
C2SMART researchers are working in partnership with Noblis to provide technical and management support for the ITS Deployment Evaluation program by populating and providing analysis of the ITS Benefits, Costs and Lessons Learned/Best Practices for an ITS Deployment Database. Additionally, C2SMART provides technical and program support for the ITS Deployment Tracking Survey and is providing technical support for modal collaboration on Evidence Based Decision Making (EBDM) to accelerate deployment.